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  1. .gitattributes +1 -0
  2. app.py +34 -0
  3. input.jpg +0 -0
  4. output.png +3 -0
  5. requirements.txt +6 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ output.png filter=lfs diff=lfs merge=lfs -text
app.py ADDED
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+ import os
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+ import cv2
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+ import tempfile
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+ from modelscope.outputs import OutputKeys
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+ from modelscope.pipelines import pipeline
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+ from modelscope.utils.constant import Tasks
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+ import PIL
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+ from pathlib import Path
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+ import gradio as gr
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+ import numpy as np
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+
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+ """Load the model into memory to make running multiple predictions efficient"""
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+ img_colorization = pipeline(Tasks.image_colorization, model='iic/cv_ddcolor_image-colorization')
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+
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+
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+ def inference(img):
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+ image = cv2.imread(str(img))
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+
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+ output = img_colorization(image[..., ::-1])
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+ result = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
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+
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+ temp_dir = tempfile.mkdtemp()
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+ out_path = os.path.join(temp_dir, 'old-to-color.png')
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+ cv2.imwrite(out_path, result)
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+ return Path(out_path)
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+
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+
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+ title = "Color Restorization Model"
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+ gr.Interface(
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+ inference,
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+ [gr.inputs.Image(type="filepath", label="Input")],
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+ gr.outputs.Image(type="pil", label="Output"),
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+ title=title
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+ ).launch(enable_queue=True)
input.jpg ADDED
output.png ADDED

Git LFS Details

  • SHA256: 7851db9dbe125f7c0405725127cf2e6fc9d905878d80b3aa251c5a9d6f5cbdc7
  • Pointer size: 132 Bytes
  • Size of remote file: 8.13 MB
requirements.txt ADDED
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+ modelscope
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+ Pillow
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+ numpy
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+ torch
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+ sentencepiece
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+ timm